This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Ovarian cancer is the leading cause of death among women with gynecologic malignancies. An estimated 21,600 cases were diagnosed in 2008 and an estimated 15,500 women died from ovarian cancer that year. Clinical applications of MRI in cancer care have increased with the development of methods to determine cellular density through diffusion-sensitive sequences, and vascular information through dynamic contrast enhanced imaging. Combined with spectroscopy, these MR imaging methods provide a wealth of functional and anatomical information to help characterize tissue. This initial project is an exploratory study across the spectrum of the clinical setting to develop functional MR methods for characterizing ovarian cancer and to evaluate their clinical utility. We propose the following Specific Aim: to acquire anatomic, metabolic and functional data from 3T MRI scans in women with normal ovaries and women with benign or malignant ovarian tumors. This study will involve both anatomic and functional imaging in women with normal ovaries, benign and malignant ovarian tumors. Ovarian pathology results will be compared against pre-surgery imaging results with the purpose of determining if imaging data show tendencies to distinguish between malignant disease, benign disease and normal tissue. The novel and innovative research that we propose will be among the first ever to combine anatomic imaging, MR spectroscopy and dynamic contrast enhanced imaging from high-field MRI of the ovary. Successful completion of this preliminary work will allow us to use high-field MRI of the ovary to address many research questions that represent challenges in ovarian cancer diagnosis and management.